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parts of the image resemble other parts of the same image. using Iterated Function System s. Fractal compression appeared to be a promising technology in the late 1980s, when in some circumstances it appeared to compress much better than JPEG , its main competitor at that time. However, fractal compression never achieved widespread use. Fractal compression is much slower to compress and decompress than JPEG. Its patents were not widely licensed. Fractal compression only has a large advantage over JPEG at low image quality levels, which is usually undesirable. The claim that fractal compressed images, when enlarged beyond their original size, looked better than similarly enlarged JPEG images seems also to have been an irrelevant distinction. It has also turned out that the most impressive examples of fractal compression require considerable human intervention: the process of generating an image from its fractal representation is easy to automate, but reversing the procedure to generate an optimal fractal representation of an image is very difficult. Most real-world images have heterogeneous mathematical properties; for instance a photograph in which mountains and clouds and trees might be represented by several classes of fractal representation; automated recognition of which class works best for which part of the image is a difficult problem in AI. Although Barnsley's Collage Theorem proves that for a large class of real-world images, compact fractal representations must exist; it does not provide a general-purpose algorithm for the construction of such representations. According to the compression FAQ , fractal image compression is sardonically known as the "graduate student algorithm": lock a graduate student in a room with a computer until they solve your problem. In practice, to achieve high image quality with compression ratios that significantly exceed those of JPEG requires significant amounts of human effort. SEE ALSO NOTES EXTERNAL LINKS
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